Effect of Internet-Delivered Emotion Regulation Individual Therapy for Adolescents With Nonsuicidal Self-Injury Disorder

Key Points Question What are the efficacy and mechanisms of change of a therapist-guided, internet-delivered emotion regulation therapy for nonsuicidal self-injury among adolescents? Findings In this randomized clinical trial that included 166 adolescents, internet-delivered emotion regulation individual therapy delivered adjunctive to treatment as usual resulted in an 82% reduction in masked assessor-rated nonsuicidal self-injury frequency (vs a 47% reduction in treatment as usual only), a statistically significant difference. Improvements in emotion regulation mediated improvements in nonsuicidal self-injury. Meaning A therapist-guided emotion regulation treatment delivered online may overcome common treatment barriers and increase availability of evidence-based psychological treatments for adolescents with nonsuicidal self-injury.

relevant to the participants' health care was noted in the electronic health record. Two senior child and adolescent psychiatrists were available for consultation based on clinical need. An individualized crisis plan was created at the face-to-face assessment and included information about who to contact in acute situations and an agreement to follow the crisis plan. The crisis plan contained necessary contact information for the study therapist and acute health care services. Further, weekly treatment conferences including a senior child and adolescent psychiatrist, a senior psychologist, and a study coordinator (psychologist) with extensive clinical experience in the treatment of self-injury were held throughout the course of the trial to monitor the safety, including adverse events, and clinical worsening of the participants. This group acted as an internal safety monitoring board that consulted with an external senior consultant (i.e., a specialist in child and adolescent psychiatry and certified DBT therapist) on an as-needed basis.

Power
Statistical power was estimated using a 0.05-level Wald test for the interaction between a binary treatment variable and linear time in treatment (12 weeks) in a zero-inflated negative binomial regression model using 200 bootstrap samples. The data for the treated group were obtained by sampling with replacement from the available data from our previous open trial of IERITA. 21

Outliers and Missing Data
The data were visually inspected using histograms (figures not shown) to check for distribution assumptions and outliers. Intermittent missing observations are common in clinical trials employing weekly measures, and it is often reasonable to assume that these data points are randomly missing. 22 Any missing observations (see Figure  1 for a breakdown of number of completed assessments) were assumed to be missing at random after visual inspection of the pattern of the missing values (eFigure 5), distribution of primary outcome across weeks (eFigure 6), and analyses of missingness in relation to study and participant characteristics. No distinct missing data patterns were identified and no differences in missing data patterns between the two treatment groups were found (eFigure 5). Furthermore, the distribution of the primary outcome across weeks did not seem to be affected by differences in missing data occurrence over time (eFigure 6). Analyses of study and participant characteristics for participants missing ≤ 20% of weekly measures versus missing > 20% within IERITA+TAU (eTable 7) and TAU only (eTable 8), as well as analyses examining between-condition differences for participants missing ≤ 20% and > 20% of weekly measures (eTable9), suggested that missingness was not strongly related to baseline characteristics within or between conditions. Corresponding analyses conducted for participants with complete data versus missing data on the primary outcome of clinician-rated NSSI at 1-month post-treatment (eTable 4, eTable 5, eTable 6) supported the same conclusion.

Self-reported NSSI Frequency Week 0 Through Week 16
The primary outcome analysis of self-reported NSSI frequency included treatment condition and weekly reports of NSSI frequency (DSHI-Y) measured once immediately before the start of treatment, once every week during treatment, and once every week for four weeks after treatment termination (1-month post-treatment). The 3 preceding pre-treatment measurement points were considered "burn-in" measures and not included in the analysis.
To examine if certain TAU or client characteristics influence the efficacy of IERITA, we ran exploratory moderation analyses examining specific aspects of TAU and client sexual orientation as moderators of treatment effects. Examined moderators included frequency of TAU (5 ordinal levels), medication (yes/no), CBT (yes/no), supportive therapy (yes/no), and unknown TAU (yes/no), and sexual orientation (heterosexual or sexual minority). Analyses used separate zero-inflated negative binomial generalized linear mixed effects regression models to estimate the rate of change in NSSI counts as a function of treatment condition and each moderator variable (in separate analyses). The models included fixed effects of time, treatment condition, moderator, and their interactions, and subject-specific random effects for intercept and linear time for the count part of the model. Frequency of TAU was centered before being entered into the model as a predictor. We investigated whether levels of the moderator influenced change over time across treatment conditions (i.e., two-way interaction between moderator and time) and differential rate of change in NSSI count as a function of treatment condition (i.e., three-way interaction between moderator, treatment condition, and time).

Effect Sizes and Data Visualization
For count outcomes, exponentiated marginal coefficients 23 (i.e., population-averaged incidence rate ratios [IRRs]) were presented, and corresponding predictions along with their 95% confidence intervals (CI) were plotted against time to visualize the development per week and treatment group. The robust sandwich estimator was used for the estimation of the standard errors. The effects for all other outcomes were evaluated with Cohen's d for mixed effects models with bootstrap (99 simulations) confidence intervals. 24 The development over time was visualized as estimated marginal means and their 95% CI.

Mediation Analysis
Parallel process latent growth curve modelling (PPGM; aka. dual-trajectory growth curve model) is a recommended method for evaluating mediation when the mediator and outcome variables are collected at repeated time points because the approach allows for estimation of individual differences in change. Further, variation in individual change in the mediator can be related to change in the outcome in a single combined growth model that also takes into account missing data and dependence due to repeated measurements. 25,26 We followed recommendations for the evaluation of PPGM. 27 Univariate latent growth models for the outcome and mediator for the weekly measured variables were specified in a similar way as those estimated with generalized linear mixed models (i.e., zero-inflated negative binomial and continuous latent growth models, respectively) and provided similar estimates of key parameters. The two univariate latent growth curve models for the outcome and mediator were combined into a PPGM. Study site and initial levels on outcome and mediator variables were statistically covaried in the model. Mediation was evaluated at the latent level (i.e., continuous random effects) and by using linear regression to relate the observed binary treatment variable (IERITA plus TAU = 1, TAU only = 0), the latent growth rate factor of the mediator, and the latent growth rate factor of the outcome (log rate count trajectory part of the model; individual variation and change in NSSI counts). See eFigure 7 for an illustration of the key elements of the PPGM employed in the current study. Mediation was supported when IERITA plus TAU, relative to TAU only, changed the latent growth factor of the mediator (a-path) and this, in turn, influenced the latent growth factor of the outcome (b-path). The point estimate of the mediated (indirect) effect (i.e., the ab-product which was the product of the a-and b-path) was significance tested with a bias-corrected bootstrapped 95% CI, constructed with the empirical distribution of 3000 bootstrap samples drawn with replacement. If the bias-corrected confidence interval did not include zero, the mediated effect was considered to be statistically significant.
Given that the magnitude of the indirect effect varies as a function of the values of the predictors in models with nonlinear associations (e.g., loglinear regression), 28 we also computed conditional indirect effects at different values of the treatment variable (0, 1) as detailed in 29,30 . These analyses revealed that the estimate of the indirect effect was slightly lower in magnitude for participants in IERITA plus TAU relative to those in TAU only, but the findings did not alter the overall conclusions about mediation and are therefore not further presented.
A sensitivity analysis was performed to examine the impact of unmeasured pre-treatment mediator-outcome confounding (aka. sequential ignorability assumption) 31 . In the sensitivity analysis, the correlation between the error terms in the mediator and the outcome growth models was fixed at different values (-.7 to .7) to determine whether this altered the point estimate and significance of the indirect effect (see Figure S9).

Statistical Software
The descriptive and outcome analyses were conducted using the statistical software R version 4.1.0. 32 The package GLMMadaptive 33 was used for fitting generalized linear mixed effects regression models and obtaining and plotting marginal coefficients. The package lme4 34 was used for fitting generalized linear mixed effects regression models, and the package ggeffects 35 was used for estimating and plotting marginal means.
In secondary analysis, we compared number of participants in each treatment condition with no NSSI episodes at 1-month post-treatment and at 3-months post-treatment using χ2. The mediation analysis was evaluated with Mplus vs. 8.1, 36 and PPGM was estimated with maximum likelihood and the EM algorithm (in combination with numerical integration) using all available observations according to intention-to-treat and the assumption of missing at random.

Blinding Integrity
Eleven families revealed their treatment condition (7 IERITA plus TAU and 4 TAU only) by accident at 1month post-treatment and 4 families revealed their treatment condition at 3-months post-treatment (1 IERITA plus TAU and 3 TAU only). Post-hoc analyses revealed that the estimates, p-values, and IRR remained virtually unchanged for 1-month post-treatment (β=-1.18; SE=0.30, < .001, IRR=0.31, 095 CI [0.33, -0.77]) and 3months post-treatment (β=-0.63, SE = 0.30, P=0.032, 95% CI [0.30, 0.94]) when the model was rerun without these ratings. Given these results, we present the models with the full sample as the main results. Excluding the eleven families at 1-month post-treatment and 4 families at 3-months post-treatment, the blinded assessors guessed the correct treatment condition in 55% of the cases at 1-month post-treatment (χ2(1)=1.26, p=.262) and 60% of the cases at 3-months post-treatment (χ2(1)=6.12, P=.013). These findings indicate that the blinded assessors were not better at guessing treatment condition than expected by change (50%) at 1-month posttreatment, but were statistically significantly better at guessing treatment condition than expected by chance at 3-months post-treatment. The majority (58%) were guesses based on the participant's reduction of NSSI and 35% were pure guesses at 1-month post-treatment. The corresponding numbers for 3-months post-treatment were 51% and 44%.
None of the examined TAU characteristics or sexual orientation emerged as unspecific predictors or moderators (i.e., none of the two-way or three-way interactions approached statistical significance).

Adverse Events
Out of the 80 participants who completed the self-report form measuring adverse events, 16 participants (20.0%) enrolled in IERITA plus TAU reported having experienced negative effects during the treatment period, of which 4 (5.0%) were determined to be related to the treatment at post-treatment. Two participants reported experiencing increased sadness (immediate effect 0 and 2; residual effects 0), one participant reported experiencing increased stress (immediate effect=1 and 2, residual effect=0), and one participant reported an increase in self-destructive behaviors (immediate effect=2, residual effect=0) because of the study. Five participants (6%) reported suicide attempts in the IERITA plus TAU condition, compared to 8 participants 24.
Feingold A. New approaches for estimation of effect sizes and their confidence intervals for treatment effects from randomised controlled trials.

Module 4 Self-validation and self-invalidation
• Common pitfalls with validation and how to handle them • Psychoeducation on self-invalidation, the contexts and situations in which it can occur, and what emotions it can lead to • Learn and practice self-validation.

Module 5
Behavioral activation • Learn about behavioral activation for yourself • Learn about behavioral activation together with your adolescent • Strategies to ask the adolescent to engage in an activity together • Suggestions of activities to do together to get more positive time together Homework: (1) Engage in activities that are enjoyable or relaxing (2) Engage in activities together with the adolescent on the adolescent's terms.

Module 6 Summary
• Summary and follow-up • Plan for continued practice of skills • Evaluation of what has been helpful • Possibility to download all the material The parents also had access to PDF-files of the adolescent modules.

eFigure 9. Sensitivity Analysis of Estimated Mediated Effect
The estimated values of the indirect (mediated) effect as a function of the sensitivity parameter (Rho), which represents the correlation between the error terms in the mediator and the outcome growth models. The bold black line and yellow bands represent the point estimates of the indirect effect and 95% bootstrapped confidence intervals, respectively.